Would kind of like to excerpt the whole post, but that feels impolite, so I'll just quote the first four paragraphs and then suggest reading the whole thing:
There’s this funny thing about Goodhart’s Law, where it’s easy to say “being affected by Goodhart’s Law is bad” and “it’s better to behave in ways that aren’t as subject to Goodhart’s Law,” but it can be very hard to explain why these things are true to someone who doesn’t already agree.
Why? Because any such explanation is going to involve some step where you say, “see, if you do that, the results are worse.” But this requires some standard by which we can judge results … and any such standard, when examined closely enough, has Goodhart problems of its own.
There are times when you can’t convince someone without a formal example or something that amounts to one, something where you can say “see, Alice’s cautious heuristic strategy wins her $X while Bob’s strategy of computing the global optimum under his world model only wins him the smaller $Y, which is objectively worse!”
But if you’ve gotten to this point, you’ve conceded that there’s some function whose global optimum is the one true target. It’s hard to talk about Goodhart at all without something like this in the background – how can “the metric fails to capture the true target” be the problem unless there is some true target?
This post reminds me of an insight from one of my uni professors.
Early on at university, I was very frustrated with that the skills that were taught to us did not seem to be immediately applicable to the real world. That frustration was strong enough to snuff out most of the interest I had for studying genuinely (that is, to truly understand and internalize the concepts taught to us). Still, studying was expensive, dropping out was not an option, and I had to pass exams, which is why very early on I started, in what seemed to me to be a classic instance of Goodhart, to game the system - test banks were videly circulated among students, and for the classes with no test banks, there were past exams, which you could go through, trace out some kind of pattern for which topics and kinds of problems the prof puts on exams, and focus only on stuying those. I didn't know it was called "Goodhart" back then, but the significance of this was not lost on me - I felt that by pivoting away from learning subjects and towards learning to pass exams in subjects, I was intellectually cheating. Sure, I was not hiding crib sheets in my sleeves or going to the restroom to look something up on my phone, but it was still gaming the system.
Later on, when I got rather friendly with one of my profs, and extremely worn down by pressures from my probability calculus course, I admitted to him that this was what I was doing, that I felt guilty, and didn't feel able to pass any other way and felt like a fake. He said something to the effect of "Do you think we don't know this? Most students study this way, and that's fine. The characteristic of a well-structured exam isn't that it does not allow cheating, it's that it only allows cheating that is intelligent enough that a successful cheater would have been able to pass fairly."
What he said was essentially a refutation of Goodhart's Law by a sufficiently high-quality proxy. I think this might be relevant to the case you're dealing with here as well. Your "true" global optimum probably is a proxy, but if it's a well-chosen one, it need not be vulnerable to Goodhart.
Would kind of like to excerpt the whole post, but that feels impolite, so I'll just quote the first four paragraphs and then suggest reading the whole thing: